Use of Multiple Contexts for Real Time Face Identification

  • Suman Sedai
  • Koh Eun Jin
  • Pankaj Raj Dawadi
  • Phill Kyu Rhee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4418)


We present the design of face identification system that can run in real time environment. We use multiple contexts to optimize the face recognition performance in real time. Initially different illumination environments are modeled as context using unsupervised learning and accumulated as context knowledge. Optimization parameters for each context are learned using Genetic Algorithm (GA).GA search the optimization parameter so as to minimize the effect of illumination variation. These weight parameters are used during similarity match of face images in real time recognition. Gabor wavelet is used for facial feature representation. Experiment is done using real time face database containing images taken under various illumination conditions. The proposed context aware method has been shown to provide superior performance than the method without using context awareness.


Face Recognition Face Image Equal Error Rate Gabor Wavelet Multiple Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Heseltine, T., et al.: Face Recognition: A Comparison of Appearance-Based Approaches. In: Proc. VIIth Digital Image Computing: Techniques and Applications, Sydney, 10-12 Dec. (2003)Google Scholar
  2. 2.
    Wiskott, L., et al.: Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 776–779 (1997)CrossRefGoogle Scholar
  3. 3.
    Cootes, T.F., Walker, K., Taylor, C.J.: View-Based Active Appearance Models. In: Proc. of the IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, 26-30 March, 2000, pp. 227–232. IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  4. 4.
    Zhao, W., Chellappa, R., Phillips, P.J.: Subspace Linear Discriminant Analysis for Face Recognition. Technical report, Center for Automation Research, University of Maryland, College park (1999)Google Scholar
  5. 5.
    Zhao, S., et al.: Enhance the Alignment Accuracy of Active Shape Models Using Elastic Graph Matching. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 52–58. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Shen, L., Bai, L.: A review on Gabor wavelets for face recognition. Pattern Anal. Applic. 9, 273–292 (2006)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Manian, V., Hernandez, R., Vasquez, R.: Classifier performance for SAR image classification. In: Proceedings of International Geosciences and Remote Sensing Symposium, IGARSS 2000, IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  8. 8.
    Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading (1989)Google Scholar
  9. 9.
    Wang, X., Qi, H.: Face recognition using optimal non-orthogonal wavelet basis evaluated by information complexity. In: Proceedings, 16th International Conference on Pattern Recognition (2002)Google Scholar
  10. 10.
    Nastar, C., Mitschke, M.: Real-Time Face Recognition Using Feature Combination. In: Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition (FG’98), Nara, Japan, 14-16 April, 1998, IEEE Computer Society Press, Los Alamitos (1998)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Suman Sedai
    • 1
  • Koh Eun Jin
    • 1
  • Pankaj Raj Dawadi
    • 1
  • Phill Kyu Rhee
    • 1
  1. 1.Dept. of Computer Science & Engineering, Inha University, 253, Yong-Hyun Dong, Nam-Gu, IncheonSouth Korea

Personalised recommendations